The most common deep learning algorithms for detection are variants of Faster-RCNN [1]} or Single-Shot Detection (SSD) [2]}.
While these algorithms have shown to be very accurate at tracking objects, most of these deep learning methods require large amounts of labeled data to train the detector.
Labeling data is usually strenuous labor, as accurate training labels require the use of a consensus label of many labeling attempts.
This data restraint is a hallmark of supervised learning.
The parameters of the model are updated based on the gradients of the error of the prediction and ground truth.
In addition, they are still slow and do not meet the real-time requirements [3]}.
Here we use an autoencoder to avoid the labelling issue.
